The filler in the asphalt mixture is essential since it plays a significant role in toughening and stiffening the asphalt. Changes in filler type can lead the asphalt mixtures to perform satisfactorily during their design life or degrade rapidly when traffic and environmental effects are considered. This study aims to assess the impact of filler types such as limestone dust (LS) and hydrated lime (HL) on Marshall characteristics and moisture damage in asphalt mixtures. Three different percentages of HL were employed in this study to partially replace the LS mineral filler: 1.5, 2.0, and 2.5% by aggregate weight. Furthermore, a control mixture was created with 7% LS by overall aggregate weight for the wearing course layer. The Marsha

Concrete is widely used in construction materials since early 1800's. It has been known that concrete is weak in tension, so it requires some addition materials to have ductile behavior and enhance its tensile strength and strain capacity to improve their uses. In this study reactive powder concrete (RPC) was used with steel fiber by using different types of cement; (Ordinary Portland cement (OPC) and/or Portland- Limestone cement (PLC)) with three types of mixtures (OPC at the first mix, 50 % OPC and 50 % PLC at the second mix and PLC at the third mix). The behavior of RPC with steel fibers on compressive strength and tensile strength of concrete with different ages of curing (7, 14, 28 and 60) days and shrinkage have been studied. The clo

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A novel ligand, (E)-5-((2-hydroxy-4,6-dimethylphenyl)diazenyl)-2,3-dihydrophthalazine-1,4- dione, was synthesized through the reaction of 3,5-dimethylphenol with the diazonium salt of 5-amino-2,3-dihydrophthalazine-1,4-dione. The ligand underwent characterization through the utilization of diverse spectroscopic methods, including UV-Vis, FT-IR, 13C, and 1H-NMR, alongside Mass spectroscopy and micro elemental analysis (Carbon, Hydrogen, Nitrogen, and Oxygen). Metal chelates of transition metals were prepared and analyzed using elemental analysis, mass spectra, atomic absorption, UV-Vis, FT-IR spectral analysis, as well as conductivity and magnetic measurements. The investigation into the compounds’ nature was conducted by utilizing mole r

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification